Web prefetch performance evaluation in a real environment

Ossa, B. de la; Gil, José A.; Sahuquillo, Julio; Pont, Ana

doi:10.1145/1384117.1384127

Cited by 5 publications

(9 citation statements)

References 11 publications

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“…Prefetching has yielded a large body of work since the birth of the Internet [1]- [16], [39]- [42]. More recently, it has been shown promising on mobile platforms [18].…”

Section: A Related Workmentioning

confidence: 99%

“…Prefetching network requests is a well-established area aimed at reducing user-perceived latency [1]- [16]. This work can be classified into two categories based on how predictions are made [17]: content-based techniques predict future network requests by analyzing application content, such as web page structure, to anticipate "sub-resources" (e.g., images, JavaScript files) that will be needed by a web page; history-based techniques predict future requests by analyzing past requests.…”

Section: Introductionmentioning

confidence: 99%

“…By contrast, history-based prefetching does not require program analysis and is platform-independent: it only relies on past requests. However, despite the large body of work targeting history-based prefetching in browsers [1]- [4], [11]- [16], such techniques remain largely unexplored on mobile platforms.…”

Section: Introductionmentioning

confidence: 99%

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Assessing the Feasibility of Web-Request Prediction Models on Mobile Platforms

Zhao

Yin

Sejfia

et al. 2020

Preprint

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Prefetching web pages is a well-studied solution to reduce network latency by predicting users' future actions based on their past behaviors. However, such techniques are largely unexplored on mobile platforms. Today's privacy regulations make it infeasible to explore prefetching with the usual strategy of amassing large amounts of data over long periods and constructing conventional, "large" prediction models. Our work is based on the observation that this may not be necessary: Given previously reported mobile-device usage trends (e.g., repetitive behaviors in brief bursts), we hypothesized that prefetching should work effectively with "small" models trained on mobile-user requests collected during much shorter time periods. To test this hypothesis, we constructed a framework for automatically assessing prediction models, and used it to conduct an extensive empirical study based on over 15 million HTTP requests collected from nearly 11,500 mobile users during a 24-hour period, resulting in over 7 million models. Our results demonstrate the feasibility of prefetching with small models on mobile platforms, directly motivating future work in this area. We further introduce several strategies for improving prediction models while reducing the model size. Finally, our framework provides the foundation for future explorations of effective prediction models across a range of usage scenarios.

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“…Prefetching has yielded a large body of work since the birth of the Internet [1]- [16], [39]- [42]. More recently, it has been shown promising on mobile platforms [18].…”

Section: A Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Assessing the Feasibility of Web-Request Prediction Models on Mobile Platforms

Zhao

Yin

Sejfia

et al. 2020

Preprint

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“…One of the papers [26] discusses the real-time evaluation of prefetching algorithms. Good number of predictive pre-fetch algorithms uses web log analysis to model user's navigation behavior [16] [18].…”

Section: Comparison Of Ppo With Existing Algorithmsmentioning

confidence: 99%

PPO - An Approach to Personalized Web Acceleration

S¹,

Suresh²

2017

IJCSIT

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“…bandwidth) [13], [3]. This scheme can be implemented in real world without modifying the HTTP standard, as demonstrated in [14].…”

Section: A Prefetching Principlesmentioning

confidence: 99%

An Intelligent Technique for Controlling Web Prefetching Costs at the Server Side

Marquez

Domènech

Gil

et al. 2008

2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology

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Prefetching is an interesting technique for improving web performance by reducing the user-perceived latency when surfing the web. Nevertheless, due to its speculative nature, prefetching can increase the network traffic and the server load. This could negatively affect the overall system performance and decrease the quality of service. To minimize and maintain under control these adverse effects, in this paper we propose an intelligent prefetching mechanism that dynamically adjusts the aggressiveness of the prefetching algorithm at the server side. To this end, we also propose a traffic estimation model that permits to accurately calculate, in the server side, the extra load and traffic generated by the prefetching. The performance evaluation study shows that our proposal effectively regulates the adverse effects of web prefetching without affecting its benefits.

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Web prefetch performance evaluation in a real environment

Cited by 5 publications

References 11 publications

Assessing the Feasibility of Web-Request Prediction Models on Mobile Platforms

Assessing the Feasibility of Web-Request Prediction Models on Mobile Platforms

PPO - An Approach to Personalized Web Acceleration

An Intelligent Technique for Controlling Web Prefetching Costs at the Server Side

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